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--- open_projects [2023/07/02 16:09] – admin
+++ open_projects [2024/02/14 17:09] (current) – project
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 If you are a potential supervisor, [[supervisor_instructions:click here]]
+=== Investigation of the effect of the circadian rhythm on the genetic control of gene expression ===
+Contact: Sonia shah <sonia.shah@imb.uq.edu.au>, Solal Chauquet <uqschauq@uq.edu.au >
+The circadian rhythm reflects the daily cycle of behaviours and metabolic processes organisms exhibit. A 24-hour gene expression pattern occurs at the molecular level, with genes activated either during the day or night. Different tissues all display circadian control, with some more affected than others. Within the liver, for example, 3000 genes are subjected to circadian control. This regulation is orchestrated by a small group of CLOCK genes, establishing feedback loops that result in rhythmic gene expression in every tissue.
+We know that gene expression can be influences by genetics variants, called expression quantitative trait loci (eQTL), and this may be one mechanism linking genetic variants to disease. As a result, large eQTL datasets have been generated to assist in understanding disease mechanisms. However, it remains unknown whether sample collection time can affect eQTL identification. This project therefore aims to identify the possible effects of the circadian rhythm on the genetic control of gene expression using the Genotype-Tissue expression (GTEx) dataset.
+During this project, you will run Python tools such as PEER and tensorQTL to identify eQTL within 49 tissues. You will subsequently investigate the associations identified and follow up on the role of the genes under circadian controls within different phenotypes.
+=== Understanding the influence of taste and olfactory perception on eating behaviour and health conditions using big genetic data ===
+Contact info: Daniel Hwang <d.hwang@uq.edu.au>
+Project description: Human perception of taste and smell plays a key role in food preferences and choices. There is a large and growing body of work suggesting that taste and smell (together known as "chemosensory perception") determine eating behaviour and dietary intake, a primary risk factor of chronic conditions such as obesity, cardiometabolic disorders, and cancer. Evidence to date is largely based on observational studies that are susceptible to confounding and reverse causation, leaving the "causal effects" of chemosensory perception on food consumption unclear. If their relationship is truly causal, flavour modification may represent a tangible way of modifying food consumption in a way that benefits public health outcomes. This project aims to: (i) elucidate the genetic architecture underlying individual differences in taste and smell perception, (ii) use this information to assess their causal effects on eating behaviour, and (iii) create a sensory-food causal network mapping individual sensory qualities (i.e. sweet taste, bitter taste, and more) to individual food items.
+=== Increasing drug success rate in human clinical trials using genomics ===
+Around 90% of drug candidates fail in human clinical trials largely due to lack of efficacy or safety concerns. This partly reflects the limitations of using in vitro and animal studies to predict the effect of compounds in humans. Recent studies highlight that drug targets backed by evidence from human genetic studies are 2 times more likely to make it to market. Human genetic data can also identify potential adverse side effects. Such information prior to embarking on human clinical trials could improve the success rate of a compound in human clinical trials and help avoid adverse outcomes for participants. This project will use statistical genomics analyses using publicly available human genomic data to predict efficacy as well as any safety concerns of compounds that are currently in the drug development pipeline.
+Project significance: Findings from this project could potentially identify new therapeutic applications for these compounds or unknown side effects, and ultimately informing future human clinical trials.
+Contact: Sonia Shah <sonia.shah@imb.uq.edu.au>
+Supervisors: You will be working with a multidisciplinary team of supervisors Prof Dave Evans, Dr Sonia Shah, Prof Glenn King, Assoc/Prof Nathan Palpant
+Familiarity with computational analyses (e.g using R or python etc) is needed for this project. Some knowledge around genome-wide association studies and statistical genomics methods such as Mendelian randomisation analysis would be beneficial
+=== Developing quiescent stem cell classifier using single cell transcriptomics ===
+Contact info: Dr Lachlan Harris (Lachlan.Harris@qimrberghofer.edu.au), Dr Olga Kondrashova (Olga.Kondrashova@qimrberghofer.edu.au)
+Quiescence is a reversible state of cell-cycle arrest, sometimes referred to as the “G0” phase of the cell-cycle. It is an adaptive feature of most adult stem cell populations, where it ensures that stem cells divide only when needed, preserving regenerative capacity. However, quiescence is also adopted by cancer stem cells to evade chemo- and radiotherapies that preferentially kill fast-dividing cells. Single-cell data promises to uncover the molecular regulation of quiescent stem cells in health and disease but the identification of these cells within these datasets is either reliant on expert knowledge and manual curation or is currently impossible, due to a lack of marker genes.
+The most common classifiers that define cell-cycle stages (G1/S/G2/M) in single-cell RNA-sequencing data (scRNA- seq) were trained on populations of actively cycling cells. Therefore, these tools cannot identify quiescent stem cells in “G0” phase of the cell-cycle. It is an outstanding question as to whether there are sufficient transcriptomic similarities across quiescent stem cells from different tissue types to build a generalisable model to discriminate these cellular populations. Furthermore, it is unknown whether such a model would generalise to cancerous tissue, where increased variability in transcriptomic states often degrades the distinction between cell types.
+This project aims to develop a broadly applicable quiescent classifier. As a first step towards this, this project will seek to 1) contribute to the curation of datasets and isolation of tissue-agnostic and tissue-specific feature sets that define quiescent stem cells and 2) compare methods for training quiescent classifiers and for determining the most salient features.
+=== Understanding sex-specific cardiovascular disease risk ===
+Contact info: Dr Sonia Shah (sonia.shah@imb.uq.edu.au), Dr Clara Jiang (j.jiang@uq.edu.au)
+Description: Cardiovascular diseases (CVD) account for 35% of female deaths globally (29% in Australia). However, CVDs remain under-studied, under-diagnosed and under-treated in women. This sex disparity is partly due to the lack of knowledge of female-specific risk factors. This project involves statistical analysis of large-scale health and genetic data to identify sex-specific CVD risk factors and underlying mechanisms.
+Requirements: A background in genetics and computational data analysis is preferable.
+=== De-risking the drug development pipeline by finding biomarkers of drug action ===
+Supervisor: Dr Nathan Palpant (n.palpant@uq.edu.au)
+Greater than 90% of drugs fail to advance into clinical approval. Genetic evidence supporting a drug-target-indication can improve the success by greater than 50%. This project aims to make use of consortium-level data resources (UKBiobank, Human Cell Atlas, ENCODE etc) to identify genetic links between genetic targets and phenotypes to help facilitate the translation of drugs from healthy individuals (Phase 1 clinical trial assessing safety) into sick patients (Phase 2 clinical trial assessing efficacy). Finding orthogonal biomarkers of drug action in healthy individuals is critical to de-risk drug dosing when transitioning from Phase 1 to Phase 2 trials. Using ASIC1a as a candidate drug being developed to treat heart attacks, we aim to develop a functionally validated computational pipeline to predict orthogonal biomarkers of ASIC1a inhibitor drug action in healthy individuals to help inform dosing in human clinical trials. Computationally predicted biomarkers will be validated using genetic knockout animals and pharmacological inhibitors of ASIC1a. Collectively, this project will help develop proof-of-principle computational pipeline for orthogonal biomarker prediction of drug targets in the human genome.
+=== Parsing the genome into functional units to understand the genetic basis of cell identity and function ===
+Supervisor: Dr Nathan Palpant (n.palpant@uq.edu.au)
+The billions of bases in the genome are shared among all cell types and tissues in the body. Understanding how regions of the genome control the diverse functions of cells is fundamental to understanding evolution, development, and disease. We recently identified approaches to define diverse biologically constrained regions of the genome that appear to control very specific cellular functions. This project will evaluate how these biologically constrained regions of the genome have influenced evolutionary processes, evaluate their regulatory basis in controlling the identity and function of cells, and analyse the promiscuity of cross-talk between different biologically constrained regions. The project will also study how these genomic regions impact disease mechanisms by evaluating how disease-associated variants in different regions influence survival of patients with cancer and assessing whether these regions are associated with identifying causal disease variants in human complex trait data. The project will involve significant collaborative work with industry partners and researchers across Australia with the goal of providing critical insights into fundamental mechanisms of genome regulation.
+=== Machine learning integration of sequencing and imaging data in cancer research ===
+Cancer is a complex disease that is difficult to treat due to the high level of variation within a tumor and between patients. To better understand cancer complexity at the tissue level, we use a combination of techniques such as single-cell sequencing, spatial transcriptomics, tissue imaging, statistical learning, and deep learning. We analyze the data using high-performance computing to computationally reconstruct biological regulatory networks underlying human diseases in every single cell and between cells within a tissue, like a tumor. By measuring both molecular profiles of the cells and their neighborhood environment, we can integrate genomics and imaging data for earlier and more accurate diagnosis and prognosis of diseases from using tissue biopsies. Our goal is to advance the understanding of biomarkers and cellular regulatory networks that are specific to cell types and tissue microenvironment, which will contribute to early disease diagnosis, targeted drug discovery, and precision medicine.
+Contact: Quan Nguyen <quan.nguyen@imb.uq.edu.au>
 === Resolving molecular trajectories of differentiation pathways of adaptive immune cells in chronic infection generating long-term memory ===
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 This project is looking for bioinformatics Masters students (ideally 16 units, but we consider 8 unit applicants as well. **Students placed overseas who want to conduct a project remotely are welcome**). We also consider Phd students.
+=== (Deep) Learning the regulatory grammar of ageing  ===
-=== Trans-ancestry conditional analyses of genome-wide association studies ===
+Contact info: Dr Christian Nefzger (c.nefzger@imb.uq.edu.au), Dr. Marina Naval-Sanchez (m.navalsanchez@imb.uq.edu.au and Dr. Ralph Patrick (ralph.patrick@imb.uq.edu.au)
-Contact: Dr Loic Yengo (l.yengo@imb.uq.edu.au)
+Aging is a gradual process of functional and homeostatic decline in living systems and the greatest risk factor for virtually all degenerative diseases. At a cellular level, epigenetic changes in the non-coding part of the genome play a major role in this functional decline.  The laboratory has access to deep profiling of age-related chromatin accessibility changes (ATAC-seq) with matched gene expression (RNA-seq) from 22 purified primary cell types across 11 tissues providing a roadmap of distinct regulatory elements including promoters and enhancers impacted by ageing.
-The experimental design of genome-wide association studies (GWAS) consists in testing the association between a large number of DNA polymorphisms and a trait of interest. Classically, these associations are tested using a simple linear regression (i.e. one at a time) framework, which cannot distinguish associations from correlated variants. To solve that issue, conditional and joint (COJO) analyses leverage the correlation structure between polymorphisms to identify subsets of variants that are jointly associated with the trait of interest. Current implementations of COJO algorithms can be applied to GWAS performed in individuals of a single ancestry, where the correlation structure between variants is constant; but they cannot yet handle meta-analyses of GWAS from diverse ancestries (e.g. East-Asian, European).
+Recently, machine learning and deep learning methods to understand the regulatory lexicon from DNA-protein interactions advanced our understanding of gene regulation ([[http://kipoi.org|http://kipoi.org]]). These methods can predict and annotate the sequence lexicon and impact of mutations at the nucleotide resolution.
-This project aims at developing a COJO algorithm to simultaneously perform variants selection and meta-analyses of multiple GWAS from participants of diverse ancestries. The research will include: (i) developing and comparing algorithms, (ii) testing the impact of violations of model assumptions through simulations and (iii) writing a C++ based software implementing this algorithm. Application of this research can improve our ability to discover genes involved in the susceptibility of common diseases.
+The project aims to:
-The ideal candidate will have a good understanding of the multiple linear regression model and will be able to efficiently program in R/Python and C++.
+.	Compare available machine learning Convolutional Neural Networks (CNNS) algorithms ([[http://kipoi.org|http://kipoi.org]]) to decode the regulatory drivers of cellular ageing.
+.	Statistically associate phenotypic variants from GWAs studies impacting the ageing regulatory lexicon.
+The ideal candidate should have an interest in machine/deep learning, CNNS and will be able to program in R/Python.
+The project is embedded in the [[https://imb.uq.edu.au/research-groups/nefzger|Nefzger lab with a major focus on “Cellular reprogramming and Ageing”]]. The applicant will be closely working with [[https://imb.uq.edu.au/profile/11267/marina-naval-sanchez|Dr. Naval-Sanchez]]  as the main supervisor.
-=== DNA sequence analysis to investigate why prevalence of adverse effects to ACE inhibitor medication differs across ancestries ===
-Contact: Dr Sonia Shah (s.shah1@uq.edu.au)
-The angiotensin converting enzyme (ACE) is a component of the renin-angiotensin pathway which regulates blood pressure. It is a target for blood pressure lowering medication (ACE inhibitors). The efficacy and occurrence of adverse side-effects from ACE inhibitor treatment is different amongst difference ancestries.
-This project will analyse exome sequence data of the ACE gene in different ancestries to determine if there are differences in structure across different ancestries, which may explain the ancestry differences in ACE inhibitor adverse effets.
-The ideal candidate will have knowledge and experience in bioinformatics, particularly DNA and protein sequence analysis and analysis of next generation sequence data. Though not necessary, experience with tools such as NCBI BLAST, samtools, vcftools, other sequence analysis packages in R will be advantageous.
+=== Decoding Transcription Factor Dosage Effects on Cell State Transitions with DoseH-Seq ===
+Contact info: Dr Christian Nefzger (c.nefzger@imb.uq.edu.au), Ralph Patrick (ralph.patrick@imb.uq.edu.au) and Marina Naval-Sanchez (m.navalsanchez@imb.uq.edu.au)
+Cell identity is controlled by different combinations of transcription factors (TFs) that bind to genomic regulatory elements to regulate gene expression. TF activity is not binary in most instances but graded and in response to TF dosage levels (e.g., Naqvi et al., Nat Genet., 2023, PMID: 37024583). For this reason, TFs are strongly enriched for haploinsufficient disease associations (Seidman et al, 2002, J. Clin. Invest. PMID: 11854316; Van de Lee et al., 2020, Trends Genet., PMID: 32451166) and TF dosage and stoichiometry strongly affects reprogramming outcomes (e.g., Polo et al, 2012, Cell, PMID: 32939092; An et al., 2019, Cell Reports, PMID: 31722212). Furthermore, TF dosage effects may also underlie seemingly contradictory effects linked to overactivation of certain TFs in cancer contexts, including of the Nfi family (Becker-Santos, 2017, The Lancet Discovery Science, PMID: 28596133).
+Single-cell RNA+ATAC-seq is a uniquely powerful assay to measure the impact of TF levels on cell regulatory architecture; however, no tools currently exist to directly study TF dosage effects on temporal cell state transitions. To address these gaps, we developed Dosage and Hashtag sequencing (DoseH-seq), an expansion of the 10x Genomics single-nucleus (sn)RNA+ATAC-seq assay that enables sensitive detection of lentiviral perturbations (e.g., TFs) linked to a heterogeneously expressed promoter. In combination with sample hash tagging, multiple temporal, and dosage states, for theoretically any number of genes of interest, can be profiled. This allows detection of TF dosage-dependent effects on temporal cell state transitions, chromatin architecture, co-factor expression, and the rewiring of TF networks at high-resolution. Compatibility with BGI sequencing technology enables the generation of low-cost datasets.We demonstrate the utility of DoseH-seq by tracking the dosage effects of somatic transcription factor, Nfix, during reprogramming towards pluripotency. Contrary to the current dogma, we find that Nfi overexpression can act either as a reprogramming roadblock or as a reprogramming booster, depending on TF dosage and context. These insights may help resolve the TF’s paradoxical role in cancer. DoseH-seq represents a powerful tool for elucidating, and ultimately controlling, both desired and pathological cell state transitions.
+The applicant would help drive method establishment around our novel DoseH-seq technique and support analysis to understand TFs dosage effects with established data sets. Ideal candidate will be able to efficiently program in R or Python. This project is looking for bioinformatics Masters students (ideally 16 units, but we consider 8 unit applicants as well. We also consider PhD students.
+=== Trans-ancestry conditional analyses of genome-wide association studies ===
+Contact: Dr Loic Yengo (l.yengo@imb.uq.edu.au)
+The experimental design of genome-wide association studies (GWAS) consists in testing the association between a large number of DNA polymorphisms and a trait of interest. Classically, these associations are tested using a simple linear regression (i.e. one at a time) framework, which cannot distinguish associations from correlated variants. To solve that issue, conditional and joint (COJO) analyses leverage the correlation structure between polymorphisms to identify subsets of variants that are jointly associated with the trait of interest. Current implementations of COJO algorithms can be applied to GWAS performed in individuals of a single ancestry, where the correlation structure between variants is constant; but they cannot yet handle meta-analyses of GWAS from diverse ancestries (e.g. East-Asian, European).
+This project aims at developing a COJO algorithm to simultaneously perform variants selection and meta-analyses of multiple GWAS from participants of diverse ancestries. The research will include: (i) developing and comparing algorithms, (ii) testing the impact of violations of model assumptions through simulations and (iii) writing a C++ based software implementing this algorithm. Application of this research can improve our ability to discover genes involved in the susceptibility of common diseases.
+The ideal candidate will have a good understanding of the multiple linear regression model and will be able to efficiently program in R/Python and C++.
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 === Comparing algorithms to estimate polygenic effects ===
-Contact info: Ben Hayes (b.hayes@uq.edu.au), for further information contact Roy Costilla (r.costilla@uq.edu.au)
+Contact info: Ben Hayes (b.hayes@uq.edu.au),
 With the advent of new genomic technologies comes the need to develop new statistical and computational algorithms that can handle large amounts of data in Animal Science. Within the Bayesian paradigm, current methods to estimate polygenic effects for complex traits rely mostly on Gibbs sampling. These approaches are not necessarily scalable to big datasets as the computation time grows more than linearly with sample size. This means that huge computational resources, in terms of RAM memory and/or computing time, need to be used to fit such models. The aim of this project is to compare the performance of alternative Markov chain Monte Carlo (MCMC) algorithms when estimating polygenic effects for complex traits in tropically adapted beef cattle. In addition to Gibbs sampling, at least two MCMC algorithms will be compared: Hamiltonian Monte Carlo and Variational Inference. The student will also learn the basics of Bayesian Statistics and High Performance Computing at UQ.
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 The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time.
+=== Develop tools to impute methylation sites from low-coverage sequencing ===
+Contact info: Loan Nguyen (t.nguyen3@uq.edu.au)
+In humans, the methylation state of CpG sites changes with age and can therefore be utilized as an accurate biomarker for aging. In cattle, biological age prediction based on methylation status could provide key information for genetic improvement programs. Additionally, comparing chronological age with biological age (based on methylation status) can provide important information about the stress an animal has been under during its lifetime.
+In this project, students will use cutting edge data sources including reduce representation bisulphite sequencing data, whole genome bisulphite sequencing, long read sequencing and human methylation data to develop a tool to impute methylation sites from low coverage ONT sequence data.
+This project is designed for students who are studying for Masters of Molecular Biology, Masters of Biotechnology, & Masters of Bioinformatics.
+Available for semester 1, 2 and summer
+=== Differential methylated regions related to puberty in Brahman cattle ===
+Puberty is a complex whole-body phenomenon that affects bone growth. In this study, we investigated how puberty in Bos indicus females affects methylation profiles in the epiphyseal growth plate, the cartilage that is essential to bone growth in long bones. Student will analyse nanopore sequencing data of 12 samples (6 pre-puberty and 6 post-puberty) to call methylation and identify the differentially methylated regions between these two groups.
+This project is designed for students who are studying for Masters of Molecular Biology, Masters of Biotechnology, & Masters of Bioinformatics.
+Available for semester 1, 2 and summer
 === CRISPR ===
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 Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.
-=== The transcriptional landscape of cardiovascular differentiation ===
-Contact info: Nathan Palpant (n.palpant@uq.edu.au)
-Project description: Analysing the transcriptional landscape of cardiovascular differentiation from stem cells at single cell resolution. Stem cells provide a mechanism for generating all cell types of the body. Understanding the mechanisms by which stem cells differentiation into these diverse cell types is central to utilizing them for understanding developmental biology and maximizing their translational potential for cell therapeutics and drug discovery. In this project, you will make use of and develop computational and statistical tools to study the transcriptional landscape of cardiovascular differentiation at single cell resolution. The project will include implementing protocols for quality control analysis, normalization, and clustering, analysing gene networks underlying cell subpopulations, identifying key genetic regulators of cell states, and helping develop novel strategies for studying and analysing single cell RNA-sequencing data to study biological questions.
-Availability, requirements, etc: The project as available on an ongoing basis for honours or masters of Bioinformatics students, full time.
 === Machine learning and data integration in bioinformatics ===
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 Availability all year, for bioinformatics students with problem-solving skills, Honours or Masters.
-=== Shared genetics and functional mechanisms underlying female reproductive disorders and related diseases ===
- Contact: Dr Sally Mortlock (s.mortlock@imb.uq.edu.au), Professor Grant Montgomery (g.montgomery1@uq.edu.au).
-The human endometrium plays a vital role in female fertility, embryo implantation, pregnancy and related diseases. Endometriosis is a disease occurring in 7-10% of women whereby tissue similar to that of the endometrium grows outside the uterus. Large scale genetic studies have identified 14 genomic regions associated with endometriosis some of which have been reported in other related diseases. This along with reports of comorbidity with other diseases has highlighted the potential of shared genetic risk loci and causal relationships between endometriosis and other diseases. Current studies integrate genetic, RNA-sequence and epigenetic data to understand how genetic variants control gene regulation and disease risk. The aim of this project is to integrate locally and externally accessible omic datasets to determine the genetic and epigenetic overlap between loci associated with endometrial gene regulation, endometriosis, other female reproductive disorders such as ovarian cancer, and related diseases including melanoma. Overlap in genomic risk loci will be tested using recently developed statistical and computational genomics tools (R, PLINK, Linux and GCTA). LD score regression will be used to estimate the genetic correlation between diseases and Generalised Summary data-based Mendelian randomisation will be used to test causal associations between these diseases, risk factors and endometriosis. Shared risk loci will be fine mapped to identify potential shared casual mechanisms. Ultimately by identifying shared genetic risk factors between diseases we hope to develop biomarkers to predict future disease susceptibility, aid in faster diagnosis and prioritise targets for functional follow-up studies and drug development. Students with a basic background in statistics and/or bioinformatics are encouraged to apply.
-Available all year, for Master of Bioinformatics students; suitable for one or two semesters, full-time.
-=== Integrating omic datasets to interrogate endometriosis risk regions ===
- Contact: Dr Sally Mortlock (s.mortlock@imb.uq.edu.au), Professor Grant Montgomery (g.montgomery1@uq.edu.au).
-Human endometrium is a highly specialised and complex tissue that plays vital role in female fertility, embryo implantation and pregnancy. Endometriosis is a disease occurring in 7-10% of women whereby tissue similar to that of the endometrium grows outside the uterus. Large scale genetic studies have identified 14 genomic regions associated with endometriosis. Recent investigations have also found associations between genetic variants and gene expression and methylation in the endometrium. The aim of this project is to integrate genetic, expression and methylation data with transcript level data from >300 endometrial samples to investigate genetic and epigenetic mechanisms regulating genes in endometriosis risk regions. Data from publically available databases such as ENCODE and Roadmap will also be downloaded to functionally annotate regions. Mapping out regulatory mechanisms in these risk regions will help prioritise target genes for functional analysis. Students with a basic background in statistics and/or bioinformatics are encouraged to apply.
-Available all year, for Master of Bioinformatics students; suitable for one or two semesters, full-time.
-=== Bioinformatics analysis/ transcriptomics of chemokines from Barramundi during bacterial infection ===
-Contact: Stuart Kellie (SCMB), s.kellie@uq.edu.au; Andy Barnes (SBS)
-=== Finding new phages in the genomes of gut bacteria  ===
-Contact: Rosalind Gilbert, ros.gilbert@daf.qld.gov.au, Department of Agriculture and Fisheries; Diane Ouwerkerk, diane.ouwerkerk@daf.qld.gov.au, Department of Agriculture and Fisheries.
-Viruses infecting bacteria (bacteriophages or phages) are highly abundant in microbial ecosystems such as those found in the gut. This project will involve finding and annotating novel bacteriophages present as prophage elements within the genome sequences of gut-associated bacteria, for example, those infecting the genera often found in ruminant livestock (for example, Ruminococcus, Bacteroides and Butyrivibrio). Prophage elements will be annotated, characterised and compared to previously identified bacteriophages. The extent to which these novel prophages are found in gut microbial ecosystems will also be determined through comparison with metagenomic datasets. This computer-based project will use bioinformatics tools to build and interrogate sequence datasets, and combines interests in microbial genetics and viral ecology.
-Available all year, for Master of Bioinformatics or Honours students; suitable for 2 unit (one day per week, 1 semester), 4 unit (2 to 3 days per week, 1 semester) projects
-=== Analysis of candidate genes for motor neuron disease ===
-Contact: Dr Marie Mangelsdorf, (m.mangelsdorf@uq.edu.au)
-Motor neuron disease (MND) is a late onset neurodegenerative disease in which the motor neurons that control muscle movement die, leading to paralysis and death usually within 3 years of diagnosis. There is no treatment. MND may be both familial or sporadic. More than 20 genes for MND have been identified largely through analysis of familial cases and for most genes, sporadic cases have also been shown to harbour mutations in the same genes. Currently mutations in these genes account for ~60% of familial cases, and 10% of sporadic cases. We are undertaking multi-faceted genomics approach of sporadic cases in order to uncover the genetic causes of sporadic MND. One aspect of this study is whole exome sequencing (WES) of sporadic cases. The Honours project will validate and investigate sequence variants in candidate genes identified by WES. Techniques used to determine pathogenicity of the variants will include polymerase chain reaction, Sanger sequencing, molecular cloning, tissue culture and microscopy.
-Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.
-=== Bioinformatics analysis to characterise species of gnathiid isopods ===
-Contact: Jess Morgan, Jessica.morgan@uq.edu.au, The University of Queensland.
-I am looking for a motivated student to help me investigate applying bioinformatics techniques to characterise species of gnathiid isopods that parasitise fish. Specifically, we aim to capture the complete mtDNA of the parasites (plus any other markers would be a downstream bonus). I will need a student capable of constructing contigs from a next gen library (either ion torrent  or illumina as determined by their lit review) then mining the contigs to scaffold against published crustacean mt genomes via Blast searches. This is a bioinformatics project for semester 2, 2014 and depending on funding may have the option to extend into semester 1, 2015. If you are interested in applying please forward a short resume and your academic record to Jess Morgan (Jessica.morgan@uq.edu.au).
-Available for Master of Bioinformatics students; suitable for one or two semesters, full-time
-=== Identification and characterization of short open reading frames ===
-Contact: Joseph Rothnagel, j.rothnagel@uq.edu.au, School of Chemistry and Molecular Biosciences, The University of Queensland.
-Short peptides (sPEPs) that are encoded by short Open Reading Frames (sORFs) are surprisingly common in eukaryote genomes. Recent bioinformatic and ribosomal footprinting studies have identified several thousand sORFs with coding potential and several sPEPs have been identified by mass spectrometry.  However, their role in cellular functions remains to be determined. In this project you will identify and characterize sPEPs using bioinformatic tools to interrogate large data sets from genomic, transcriptomic and proteomic experiments. You will help to determine the contribution of sPEPs to the human proteome, and  provide insights into their roles.
-Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.
 === Reconstruction of ancestral proteins ===
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 The project is available throughout the year for students with bioinformatics knowledge (Honours or Masters). This project cannot be carried out remotely due to ethics restrictions on data sharing.
-=== Identifying genomic predictors of progestin response in endometrial cancer ===
-Contact: Dr Olga Kondrashova (Olga.Kondrashova@qimrberghofer.edu.au)
-Endometrial cancer is the most common gynaecological cancer. Surgery is the standard treatment, however it can be unsafe for elderly or obese women, as well as it results in fertility loss for young women. Hormone therapy using progestin is an alternative treatment to surgery, but less than 70% of endometrial cancers respond to progestin. Therefore, there is a need for predictive biomarkers. The exact biomarkers for predicting responses to progestin are yet to be defined.
-The focus of this project is on identifying genomic biomarkers of progestin response using endometrial cancer samples from the feMMe clinical trial. Around 100 cancer samples will be analysed using a comprehensive targeted DNA sequencing assay, which covers all key endometrial cancer genes and assesses tumour mutation burden and microsatellite instability. The work in this project will involve assessing the suitability of samples, coordinating molecular profiling, and bioinformatic analysis of the genomic data.
-The project is available for early-2023 start for students with bioinformatics knowledge (Honours or Masters). It is a wet and dry lab hybrid project, so previous laboratory experience is required.
-=== Studying genomic heterogeneity in lung adenocarcinomas ===
-Contact: Dr Olga Kondrashova (Olga.Kondrashova@qimrberghofer.edu.au)
-Lung cancer is the most common cause of cancer deaths in Australia and worldwide and an important area of unmet clinical need. Half of lung cancer patients present with metastatic disease and have poor long-term outcomes, with a 5-year survival of only 5%. The introduction of targeted therapies and immune checkpoint inhibitors have been a major advance in the treatment of lung cancer, however, only a small proportion of patients experience durable responses to these therapies. Understanding the cancer heterogeneity and molecular mechanisms underpinning treatment response as well as acquired resistance, is critical to develop novel effective therapeutic strategies.
-This project will explore inter- and intra-patient genomic and transcriptomic heterogeneity of lung adenocarcinomas in the context of treatment response and patient outcome by utilising publicly available resources (TCGA and MSK-IMPACT), and data generated from pre-clinical models. The project will use various bioinformatic packages to analyse and present the data, but will also require development of custom code.
-The project is available throughout the year (2023) for students with bioinformatics knowledge (Honours, Masters or PhD). This project cannot be carried out remotely due to ethics restrictions on data sharing.